Industrial Strength Empirical Modeling clearly explains the main principles of the different machine learning approaches and offers a methodology of how to integrate these techniques for successful real world implementation. It covers the conceptual foundations of selected machine learning approaches like traditional neural networks, analytical neural networks, support vector machines, and particle swarm optimizers at a moderate theoretical depth. Once this foundation is established it provides a framework for successful industrial application of these soft computing techniques including issues of data integrity, sensitivity analysis, model deployment and maintenance, value assessment, and empirical modeling project management. Likewise, early in the book, simple examples illustrate the fundamental concepts; later, case studies are introduced to better address the complex nature of real-world applications. Throughout the text, the authors provide "mind maps" to help readers visualize and consolidate concepts.